Tagged “vector-search”

3 posts.

All Model Architecture23 Training & Alignment22 Inference & Serving24 Agents & Orchestration12 Reasoning & Evaluation28 Safety, Security & Governance9 Platforms & Practice22

Reasoning & Evaluation 24 min

The Curse of Dimensionality: Why Distances Stop Meaning Anything, and Why Learning Works Anyway

Scatter 1,000 random points in a 1,000-dimensional cube and the farthest one from a query is only about 12% farther away than the nearest. By that arithmetic nearest-neighbour search should be meaningless, and yet every vector da…

supervised-classical learning-theory embeddings vector-search ∑ ◫

Reasoning & Evaluation 24 min

The Leaderboard Is Not Your Corpus: Why Top-Ranked Embedding Models Disappoint in Production

Embedding models are chosen from a leaderboard more often than from an experiment, and the leaderboard now publishes training splits for its own test sets. Between contamination, task-family averaging and geometry no benchmark me…

embeddings retrieval-rag evaluation benchmarks ∑ ◫

Inference & Serving 30 min

What a Vector Index Actually Costs: The Router, the Quantiser, and the Recall Ceiling

In the NeurIPS'21 billion-scale ANN challenge, entrants were ranked on recall at a fixed 10,000 queries per second. The best standard-hardware submissions landed around 0.71 to 0.79 recall@10, not 0.99. Every vector index is two …

vector-search ann retrieval hnsw ∑ ◫